Dynamic Stability Analysis of a Simplified Neuro-Fuzzy Direct Torque Control Scheme for a Grid-Connected DFIG-WECS With Improved Performance and Reduced Computation

This paper presents a simplified neuro-fuzzy torque control (NFTC) scheme for a grid-connected doubly fed induction generator (DFIG) with enhanced stability while reducing the computational burden. The proposed NFTC scheme processes the torque and flux errors between the actual torque and flux and their respective references to produce switching signals to the Rotor Side Converter (RSC). Furthermore, the NF structures utilized by the proposed NFTC scheme are more simplified as they consider single input to generate control signal. A systematic analysis related to the computational burden of the NF networks is performed to prove the reduced computation related to proposed NFTC. A hybrid training algorithm is also developed to train the parameters of the proposed NF structures based on the data obtained from the classical PI controller incorporating system uncertainties. The stability analysis of the WECS incorporating the proposed NFTC scheme is accomplished by estimating the system to a second order linear time invariant system. Furthermore, the trajectories of generator torque and flux are analyzed considering grid voltage fluctuation to verify the global stability of the WECS. The performance of the NFTC is investigated in simulation using MATLAB-Simulink at various operating conditions such as wind speed change and grid voltage variations. The efficacy of the NFTC is also verified experimentally using laboratory prototype and DSP board DS1104. Both simulation and real-time results confirm the satisfactory performance of the proposed NFTC scheme.